|Publication number||US6546119 B2|
|Application number||US 09/578,815|
|Publication date||Apr 8, 2003|
|Filing date||May 24, 2000|
|Priority date||Feb 24, 1998|
|Also published as||DE60134858D1, EP1301895A2, EP1301895A4, EP1301895B1, US20020141618, WO2001091353A2, WO2001091353A3|
|Publication number||09578815, 578815, US 6546119 B2, US 6546119B2, US-B2-6546119, US6546119 B2, US6546119B2|
|Inventors||Robert Ciolli, Peter Whyte, Gurchan Ercan, Andrew Mack|
|Original Assignee||Redflex Traffic Systems|
|Export Citation||BiBTeX, EndNote, RefMan|
|Patent Citations (12), Non-Patent Citations (2), Referenced by (172), Classifications (8), Legal Events (15)|
|External Links: USPTO, USPTO Assignment, Espacenet|
The present application is a continuation-in-part of the following co-pending U.S. Patent applications: U.S. Patent application entitled, “Vehicle Imaging and Verification”, having U.S. application Ser. No. 09/028,675, filed Feb. 24, 1998, pending; and U.S. Patent application entitled, “Digital Image Processing”, having U.S. application Ser. No. 09/028,360, filed Feb. 24, 1998, U.S. Pat. No. 6,240,217, which claims the benefit of Australian Application No. P05258, filed Feb. 24, 1997. Both of these parent applications are assigned to the assignee of the present application.
The present invention relates generally to computer networks, and more specifically to a system for monitoring the occurrence of traffic offenses and providing photographic evidence of offenses for use by traffic enforcement agencies.
Enforcement of traffic laws is a major undertaking for law enforcement agencies around the world. Large-scale automated photo enforcement technologies provide powerful tools to modify unsafe driving behavior by educating communities that unsafe driving will be penalised. The most effective programs combine consistent use of traffic cameras supported by automated processing solutions that deliver rapid ticketing of traffic violators, with other program elements including community education and specific targeted road safety initiatives like drunk-driving enforcement programs and license demerit penalties.
Automated traffic law enforcement addresses the multi-billion-dollar problem caused by non-compliant driving behavior, such as speeding and red light running, illegal turns, and other violations. In the United States, such non-compliance has been estimated to account for about one-third of all traffic crashes and two-thirds of the resulting fatalities.
Over the years, crash statistics have deteriorated due to the ever-growing number of vehicles on the road and the increasing vehicle-miles traveled, and this situation is becoming a major concern of Federal, State and local authorities. Realizing that the option of intensifying conventional police enforcement is limited by manpower and budgetary constraints, authorities are now turning to automated enforcement to provide an effective alternative that also releases police for other enforcement duties.
Although certain countries have used photo-enforcement with some degree of success, current systems of traffic enforcement using photographic techniques have disadvantages that generally do not facilitate effective automation and validation of the photographs required for effective use as legal evidence.
Present methods of automated traffic enforcement typically involve the use of traditional 35 mm celluloid film based cameras and photographic techniques to acquire the photographic evidence of traffic offenses. Although limited success has been achieved with this present technology, many inherent limitations and poor efficiency outcomes limit the programs' effectiveness. Tangible benefits of automated traffic enforcement in Australia and other user countries have been achieved despite the inherent limitations of wet-film-based traffic camera technologies. However, because such systems have been the only viable imaging system available for such use, widespread acceptance and implementation has not been achieved.
Ensuring the security and integrity of the original photographic evidence is also a major disadvantage of present traffic enforcement systems. The best film-based traffic camera programs in the world rely on a combination of strict physical storage procedures for developed film negatives, and sworn officer statements, to prove the validity of their evidence. Early digital camera protocols tended to mimic these procedures, as well, by requiring that digital images be stored on WORM diskettes or other hard disk media. Such protocols allow operators to hold ‘original’ evidence in their hands and physically lock it away in the same way as they lock away ‘original’ film negatives in film registries. While the solution may feel comfortable, these systems are susceptible to security breaches.
Developed film negatives do not hold truly original evidence. By the time the first negative has been created, there has been significant technical and human intervention during the collection, transfer and development processes. In addition, relying on the medium and protocols of storage as the only form of security is flawed, whether the evidence is being held in digital or film format. Time consuming though it may be, film negatives can be digitized, altered, and re-shot. There is no obvious way of knowing if this has happened because film technology, unlike digital technology, offers no inherent ability to construct an electronic audit trail on the life of an image that guarantees its authenticity from the moment of capture onward.
The same potential to alter digital evidence exists also. Without application of cryptography technologies images stored to disks can be copied and altered without detection. Under this scenario, no court would be able to tell the difference between original digital evidence and altered evidence. As with film, all that would be known is who has had the disk, when it was created and where it has been, provided these records are accurate. Thus present analog and digital photography methods of capturing traffic violation evidence do not necessarily implement adequate security measures commensurate with their use as legal evidence of a violation.
It is an object of embodiments of the present invention to increase the efficiency and effectiveness of photographic traffic violation monitoring systems.
It is a further object of embodiments of the present invention to improve the performance, reliability and overall economics of automated traffic enforcement programs.
It is a further object of embodiments of the present invention to provide a method of image authentication that is independent of the technology used to transmit, store and process the images.
It is yet a further object of embodiments of the present invention to provide a traffic violation monitoring and recording system that provides secure storage and transmission of photographic images of traffic violations.
A system for monitoring and reporting incidences of traffic violations at a traffic location is disclosed. The system comprises a networked digital camera system strategically deployed at a traffic location. The camera system is remotely coupled to a data processing system. The data processing system comprises an image processor for compiling vehicle and scene images produced by the digital camera system, a verification process for verifying the validity of the vehicle images, an image processing system for identifying driver information from the vehicle images, and a notification process for transmitting potential violation information to one or more law enforcement agencies.
Other features and advantages of the present invention will be apparent from the accompanying drawings and from detailed description that follows.
The present invention is illustrated by way of example and not limitation in the figures of the accompanying drawings, in which like references indicate similar elements, and in which:
FIG. 1A is a block diagram that illustrates the overall traffic violation processing system, according to one embodiment of the present invention;
FIG. 1B is a table that outlines some of the information transferred along the data paths illustrated in FIG. 1A for an exemplary traffic violation monitoring and reporting incidence;
FIG. 2 illustrates a photographic image and accompanying reporting information provided by the camera system and data processing system of FIG. 1A, according to one embodiment of the present invention;
FIG. 3A is a block diagram illustration of a multiple element CCD intersection camera system, according to one embodiment of the present invention;
FIG. 3B illustrates the multiple element camera system of FIG. 3A in conjunction with a synchronous timing source, according to one embodiment of the present invention;
FIG. 4A illustrates a histogram of a pixel intensity for an intersection image, according to one embodiment of the present invention;
FIG. 4B illustrates the histogram of FIG. 4A with the license plate image isolated from the background scenery image;
FIG. 5 illustrates an infringement set provided by an imaging processing system, according to one embodiment of the present invention;
FIG. 6 is a flowchart that illustrates the steps that are executed by the central processor when incident information is received from an intersection camera system, according to one embodiment of the present invention;
FIG. 7 illustrates the DMV details area of the verification screen, according to one embodiment of the present invention;
FIG. 8 illustrates a DMV lookup screen, according to one embodiment of the present invention;
FIG. 9A illustrates an example of a police authorization module interface screen, according to one embodiment of the present invention;
FIG. 9B illustrates an example of a court interface screen generated by the court interface module, according to one embodiment of the present invention;
FIG. 10 is a flowchart that illustrates the steps of creating a traffic offense notice, according to one embodiment of the present invention;
FIG. 11 illustrates a notice preview displayed in a user interface screen, according to one embodiment of the present invention;
FIG. 12 illustrates the traffic camera office infringement processing system components, according to one embodiment of the present invention; and
FIG. 13 illustrates the components of an image analysis expert system, according to one embodiment of the present invention.
A digital automated system for monitoring and reporting incidences of traffic violations is described. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide an understanding of the present invention. It will be evident, however, to those of ordinary skill in the art that the present invention may be practiced without the specific details. In other instances, well-known structures and devices are shown in block diagram form to facilitate explanation. The description of preferred embodiments is not intended to limit the scope of the claims appended hereto.
FIG. 1A is a block diagram that illustrates the overall traffic violation processing system, according. to one embodiment of the present invention. The main components of the traffic violation processing system 100 comprise the intersection camera system 102, the data processing system 104, the police department interface system 106, the motor vehicle department interface 108, the court interface 110.
When an alleged offender 101 commits an offense at an intersection, the red light cameras in the intersection camera system 102 sense and record the event and sends the photographic data to the data processing system 104. The data processing system 104 then performs various data processing steps to verify and validate the driver and offense data. The data processing system 104 itself includes various components, such as central processor 132, file server 134, database 136, verification module 138, quality assurance module 140, and notice printing module 142. The data processing system 104 receives data from various external sources, such as the intersection cameras and motor vehicle agencies, and processes the data for further action by the appropriate law enforcement agencies.
As illustrated in FIG. 1A, various items of information regarding the driver and the vehicle are obtained by the data processing system 104 from selected authorities, such as a motor vehicle department through the motor vehicle department interface 108, and a police department through the police department interface 106. When the information relating to the offense is deemed to be valid, it is appropriately presented through the court interface system 110 to the appropriate court authorities.
As illustrated in FIG. 1A, various data paths, numbered 1 to 14, are provided among the components and sub-components of system 100. FIG. 1B is a table that outlines some of the information transferred along these data paths in a typical traffic violation monitoring and reporting incidence. Together, Table 150 in FIG. 1B, and the data paths shown in FIG. 1A constitute a data flow process for the traffic violation processing system 100.
If the red light cameras in the intersection camera system 102 detect a violation incident, a number of images (typically, four) of the incident, along with associated data (such as time and vehicle speed) are captured and transmitted to the central processor 132 of the data processing system 104. These images and the associated data comprise the primary evidence of the violation and are saved in the primary images file server 134. The central processor produces compressed scene images and incident details, and transmits these to database 136 for storage. In one embodiment, a violation is detected though the use of known wireless transmission methods, such as radar or similar waves, or through light beam detection methods, or similar techniques to determine whether a vehicle is traveling too fast or has run a red light or stop sign.
The images captured by the intersection camera system typically include at least one image of the vehicle committing the violation (i.e., running the red light), as well as images of the vehicle license plate and driver's face to provide car and driver identification information. The license plate and driver's face images are transmitted from the primary image file server to the verification module 138. Based on the vehicle license plate information, the details of the vehicle and its owner are then accessed at an appropriate motor vehicles department 108, and transmitted to the database 136.
The incident details and compressed images stored in the database 136 are next sent to the quality assurance module 140. Once the quality assurance module has checked the incident data for accuracy and integrity, the details and compressed images are sent to an appropriate police agency 106. If the police authorize a notice to be sent to the identified driver, notice details are sent to the appropriate court 110 by the data processing system 104. The notice and incident details are also transmitted from the database 136 to the notice printing module 142 of the data processing system 104. The prepared notice is then sent to the alleged offender 101 by the data processing system 104. Follow-up correspondence, such as payment reminder letters, may be sent to the alleged offender from the court 110. The alleged offender may then submit payment or make a court appearance to satisfy the notice. A notice of the disposition of the violation is then sent from the court 110 to the data processing system 104 and stored in the database 136. This completes the data processing loop for a typical violation, according to one embodiment of the present invention.
The structure and operation of the sub-components of each of the main components of traffic violation processing system 100 will be described in greater details in the description that follows.
Intersection Camera System
A typical enforcement application of the digital camera component 102 of system 100 is in the area of red-light offense detection. For this application, the camera system 102 is strategically placed at an intersection to monitor and record incidences of drivers disobeying a red light. When a vehicle is detected approaching the stop line of a monitored lane, it is tracked and its speed is calculated. If the vehicle is detected entering the intersection against the traffic signal, an evidentiary image set is captured. The event of the images being captured and the relevant details recorded is referred to as an ‘incident’, which may be defined as a potential offense. In one embodiment of the present invention, the evidentiary set consists of four incident images comprised of the following: a scene shot A, which is a scene shot of the intersection prior to the incident vehicle crossing the stop line; scene shot B, which is a scene shot of the intersection when the incident vehicle is seen to have failed to obey the traffic signal; frontal face zoom shot that attempts to identify the driver of the incident vehicle; and a license plate zoom shot that attempts to isolate the vehicle's license plate area only to identify the vehicle. In one embodiment, the images captured by the digital camera system 102 are in TIFF format, although other digital formats are also possible.
In one embodiment of the present invention, the individual incident images are captured by separate cameras or imaging elements within the digital camera system 102. For this embodiment, one imaging element generates a single image of the individual incident images. For example, one imaging element generates the face shot, another generates the license plate shot, and so on. Alternatively, the individual incident images could be produced from a single image generated by a single camera within the digital camera system, such as by producing sub-images cut from portions of the larger single image. The individual images could also be produced by generating composites of images generated by separate imaging elements within the digital camera system 102.
In relation to a potential violation, there are a number of details recorded for each image. These include, the date and time of the incident, the location of the incident, the lapsed time since the traffic signal turned red, and the camera identification.
The captured data is assigned a ‘digital signature’, encrypted, and then transmitted from the digital camera system 102 to the central processor 132 in the. data processing system 104. All four shots when transmitted have their incident details “stamped” on them. In one embodiment, this “stamped information” is embodied in a data bar that appears at the top of images seen at verification process 138 of the data processing system 104. Each of the four shots is individually identifiable as being of a particular type, i.e., scene A, scene B, face shot, and plate shot. FIG. 11 represents a Notice to Appear that includes the photographic images and accompanying reporting information that is provided by the camera system and data processing system of FIG. 1A, according to one embodiment of the present invention. As can be seen in FIG. 11, the four photographs include the driver's face shot, the license plate shot, and the scene A and scene B shots. The composition and production of the Notice to Appear illustrated in FIG. 11 will be described in greater detail below.
The intersection cameras may be controlled remotely to facilitate system analysis checks and to take test shots. For test diagnostics, a log of captured test shots are recorded. Test shots can be treated as normal and exported to the data processing system for insertion into the database as with ‘ordinary’ shots. Should it become necessary to prove to a court that a camera system was operating correctly at the time a particular incident was detected, the test shots form part of the chain of evidence, which is used to provide evidence of the cameras functioning correctly.
The intersection camera systems are inter-connected at the detection site to provide the required camera and flash coordination. Each camera is strategically located to provide the optimum field of view for the desired captured image. The enforcement camera that is equipped/interfaced with the vehicle tracking technology is positioned to effectively record both scene images as well as the license plate area shot. A supplement camera can be positioned to image the offending vehicle driver. The camera systems are interconnected using standard local area network typologies. The camera systems 102 also manage sending secure (encrypted) incident data and image information to the data processing system 104 over a computer network line, such as modem and telephone line.
In one embodiment of the present invention, the traffic violation processing system 100 utilizes digital camera technology to implement the intersection camera system 102. Such a digital camera system targets specific areas of interest with a system consisting of several imaging elements. The advantage of such a configuration is the targeting of resolution where it is needed, while preserving the rationale that the extracted images are captured at the same moment in time.
In one embodiment of the present invention, Charge Couple Device (CCD) imaging elements are used which provide spatial and dynamic resolution equal to or better than 35 mm celluloid based film. In the intersection camera system 102, a scaleable multi-element digital camera system designed specifically for traffic enforcement applications is used. This camera system is specifically designed to address the issues of image resolution, dynamic range, and imaging rates (i.e., frame per second) towards the special requirements of offense prosecutability where the images form the primary evidence.
A CCD is an image acquisition device capable of converting light energy emitted or reflected from an object into an electrical charge that is directly proportional to the entering light's intensity. This charge or pixel can then be sampled and converted into the digital domain. The digital pixel information is cached and transferred to RAM (Random Access Memory) in a host computer system in bursts via a local bus where further processing and final storage occurs.
The fundamental imaging requirement for prosecutability of an image is clear identification of the offense committed and identification of the offending vehicle. In a multiple camera system, each imaging element must be synchronized and triggered concurrently to ensure all captured images correlate the same event that is the exact time base.
FIG. 3A illustrates a multiple element CCD intersection camera system, according to one embodiment of the present invention. Camera system 300 in FIG. 3A illustrates a representative camera system comprising a primary CCD 302 and two secondary CCDs 304 and 306. The CCDs 302, 304, and 306 convert the incoming light into electronic charge. The charge is then moved through an analog shift register to provide a serial stream of charge data, similar to a bucket brigade. For camera system 300, image data from primary CCD 302 is processed through an ADC (Analog to Digital Converter) process 308 to produce digital data streams 310. The image data from the two secondary CCD cameras 304 and 306 are each processed through respective ADC processes 312 and 314 and input to a multiplexer 316 to produce digital data streams 318.
Although FIG. 3A illustrates a camera system comprising three separate imaging elements, it should be noted that the camera system used in accordance with embodiments of the present invention could include various numbers of individual imaging elements. In one embodiment, the camera system includes separate imaging elements that provide the scene and driver's face and license plate images illustrated in FIG. 11.
The basic operation of the CCD in camera system 300 is next described. For each camera, the CCD image sensing area is configured into horizontal lines containing several pixels. As light enters the silicon in the image sensing area, free electrons are generated and collected inside photosensitive potential wells. The quality of the charge collected in each pixel is a linear function of the incident light and the exposure time. After exposure, the charge packets are transferred from the image area to the serial register at the rate of one line per clock pulse. Once an image line has been transferred into the serial register, the serial register gate can be clocked until all of the charge packets are moved out of the serial register through a buffer and amplification stage producing an analog signal. This signal is sampled with high-speed ADC devices to produce a digital image.
Color sensing is achieved by laminating a striped color filter with RGB (Red, Green, Blue) organization on top of the image sensing area. The stripes are precisely aligned to the sensing elements, and the signal charged columns can be multiplexed during the readout into three separate registers with three separate outputs corresponding to each individual color. Each red, green, and blue pixel from the CCD is processed by a high-resolution analogue to digital converter capable of high sampling rates. Once in the digital domain, the pixel charge is held in cache as it waits for a data transfer window to be made available by the host computer system for transfer into host RAM.
In one embodiment of the present invention, the image data is transferred from the CCDs 302, 304, and 306 to the host system RAM 322 using a PCI (Peripheral Component Interconnect) interface 320. For many present computer systems, PCI has become the local bus standard for interconnecting chips, expansion boards, and processors. The original PCI architecture implements a 32-bit multiplexed address and data bus.
In accordance with standard PCI usage, in camera system 300, communication between devices on the PCI bus occurs through a mechanism of burst transfers. A burst transfer consists of the establishment of a bus master (an I/O cycle—in order for the initiator of the burst to attain master status on the bus) and the bus slave (target) relationship. The length of the burst is negotiated at the beginning of the transfer, and may be of any length. At burst completion, the receiving end (target) terminates the communication after the pre-determined amount of information has been received. Only one bus master device can communicate on the bus at a time. Other devices cannot interrupt the burst process because they do not have master status.
The integration of the CCD imaging device directly into the final processing computer system short cuts the traditional process of capturing digital images through video based cameras, converting the composite analog signal into a digital image with the use of ‘Frame Grabber’ and then importing the resultant image into the host computer for processing. The losses in image quality that occur due to the digital-analog-digital conversion in these systems, limit their application for traffic enforcement purposes. Furthermore, video based cameras are typically limited in resolution and dynamic range.
Dynamic resolution is an important characteristic of the camera system 300. Dynamic resolution defines the size of each pixel data once converted into digital form. The relationship is proportional to the CCD camera's ability to represent very small and large light intensity levels concurrently (i.e., the Signal to Noise Ratio, SNR) and is represented in Decibels (dB). Accordingly the sampling ADC is matched to exhibit an equivalent SNR.
The application of dynamic resolution in enforcement programs provides for a mechanism of identifying vehicle license plates with retro-reflective composites. When flash photography is used in the reproduction of high quality images, the light energy that is directed towards the license plate area is reflected back at a level (result of a high reflection efficiency), that is higher then the average intensity entering the camera. Consequently an optical burn effect (i.e. over exposure) appears around the area of the license plate.
The effect of optical burn, or “plate burn” is minimized with the utilization of a CCD and ADC system with a dynamic range capable of resolving the resultant intensity spectrum. A histogram of the image will reveal all scene and license plate details residing at opposing ends of the spectrum.
The license plate having the strongest intensity will appear at the highest levels and the rest of the image proportioned across the rest of the spectrum. However, most computing systems, and indeed the human eye, can only resolve 256 levels (or 48 dB=8 bits) of intensity. Typical 35 mm Celluloid film of 100 ASA is considered to have 72 dB of equivalent dynamic resolution. This dynamic range can resolve 4096 level of intensity and is represented by a 12-bit word.
In one embodiment of the present invention, to limit the volume of data and information kept for evidentiary purposes, a process of “Histogram Slicing” is used to scale down the overall pixel data size from 12 bits down to 8 bits by selecting only 256 of the available 4096 levels. The selection criteria will ensure that the visual integrity of the image is ensured but will also normalize the overall appearance such that overexposed areas are in balance with the rest of the image. Ideally the process would be a non-linear function that is adaptive in nature to compensate for ambient and exposure conditions. The translation for speed and efficiency would be a mapping (or lookup) function.
FIG. 4A illustrates a histogram of pixel intensities for an intersection image, according to one embodiment of the present invention, and FIG. 4B illustrates the histogram of FIG. 4A with the license plate image isolated from the rest of the images that make up the vehicle and background scene. Details of the digital imaging process that isolates the license plate image are described in related U.S. Pat. No. 6,240,217, entitled “Digital Image Processing”, which is hereby incorporated by reference. The histograms of FIGS. 4A and 4B illustrate the intensities of individual pixels in a traffic violation image on a pixel 402 axis versus intensity 404 axis. As illustrated in FIG. 4A individual pixel components for the license plate are shown as elements 408 against the pixel components for the background scene 406. Using compression and isolation imaging techniques, the intensity of the pixels for the license plate 408 are altered relative to the intensity for the pixels for the background 406, as illustrated in FIG. 4B. In this manner, the license plate is made more readable relative to the background scenery. It should be noted that the same technique could be applied to other images and components of images, such as to enhance the driver's face relative to the car.
As stated above, a typical enforcement application of the digital camera system illustrated in FIG. 3A is in the area of red-light offense detection. The camera system is strategically placed at an intersection to monitor and record incidences of drivers disobeying a red light. In one embodiment, the primary evidence produced is a set of two images. The first image showing a view of the intersection that encompasses the traffic light of the monitored approach, the offending vehicle prior to crossing the violation line (typically a white line such as a cross-walk) and sufficient background scene depicting the driving conditions at the time of the offense. The second image is typically of the same field of view but with the offending vehicle completely crossed over the violation line in conjunction with the red light.
The main area of interest is the vehicle position before and after the intersection. Although the overall resolution for this image is not critical, sufficient detail must exist to resolve features of the intersection as well as traffic signal active phase. However, in order to identify the offending vehicle the license plate details and jurisdictional information must be legible.
For 35 mm wet film cameras the effective spatial resolution must be on the order of 3072×2048 pixels. Even then the license plate details only represent 5 percent of the total number of pixels.
The architecture of the digital camera system 300 allows for the synchronous operation of multiple image elements acquiring specific area of interest all at the same interval of time. The field of view of the primary imaging element will encompass the complete intersection, the traffic signal head of the monitored approach and the offending vehicle relative position. The secondary imaging elements can be used to image the license plate area of the offending vehicle.
To ensure synchronism between each of the imaging elements the timing generators for each CCD is reset simultaneously and clocked by a single source. FIG. 3B illustrates the camera system 200 of FIG. 3A in conjunction with a synchronous timing source. Each of the three CCDs 302, 304, and 306 have their output signals synchronized to respective timing generator circuits 330, 332, and 334. The timing generator circuits are driven by common clock 340 and reset signals 342. The result is that each CCD will acquire and discharge the image simultaneously with the other CCD cameras. One benefit of the synchronous operation of the CCDs is that a single flash can be triggered with the resultant exposure recorded by all the CCDs.
In many circumstances, the vehicle detection system used in the tracking and identification of offending vehicles can provide actual vehicle position information such as the travel lane, speed, and direction which can be used to tighten the field of view of the secondary imaging elements, thus allowing a sharper and larger license plate area image. For example in a two-lane intersection or road environment, one of the secondary elements can be used to image one lane and another used to image the other lane. The advantage of this system is that two secondary cameras can share the same data path as either one lane or the other will only be imaged.
In many circumstances more than one camera system (incorporating the host computer, imaging elements and enforcement logic) may require supplemental camera systems to provide additional or more optimal fields of view of the offense. One such requirement is the acquisition of the offending vehicle driver's image where the primary detection camera is imaging the offending vehicle from behind as it approaches the intersection. In such cases it is impossible to achieve the required field of view resulting in the addition of a supplemental camera system.
In one embodiment of the present invention, distributed computer and network technologies, such as DCOM (Distributed Component Object Module) and the equivalent CORBA (Common Object Request Broker Architecture), are implemented by the traffic enforcement system 100 to provide a mechanism of seamless imaging element attachments. This allows for the effective increase in the number of imaging elements, while still preserving the single enforcement camera system ideology.
Data Processing System
As illustrated in FIG. 1A, the images captured by the intersection camera system 102 are processed in data processing system 104. Data processing system 104 includes a central processor 132, a primary images file server 134, a verification module 138, a quality assurance check module 140, a database 136, and a notice printing module 142.
The central processor 132 executes the main software program that implements the traffic violation monitoring and reporting system. The central processor 132 is designed to manage the remote camera systems and receive their incident data and image information via modem. The central processor contains its own database for recording camera system information, but also sends information to the main database 136 in the data processing system 104 for each detected incident or test shot.
FIG. 6 is a flowchart that illustrates the steps that are executed by the central processor 132 when incident information is received from an intersection camera system 102, according to one embodiment of the present invention. In step 602, four images in an appropriate digital format (e.g. TIFF format) are stored on the primary images file server 134 in an area which is regularly archived and which is available for read-only access by verification users. These images constitute the primary evidence, which is digitally signed to prevent any subsequent undetected manipulation. The four images typically consist of two scene images, a driver's face image, and a license plate image.
In step 604, compressed images in JPEG format are made of the two scene images. An incident record is then stored in the main database 136 with associated records containing the two compressed scene images and the address path of the face and plate TIFF images, step 606. The incident record is assigned a unique incident number; which is used to link it to all other associated records throughout its lifecycle.
The verification module 138 within the data processing system 104 allows trained operators to check that all of the legal and business rules relating to the incident have been met in the captured images and data. That is, the operators verify that the incident is a legitimate offense and that the driver can be readily identified. In one embodiment of the present invention, when a user logs onto the verification module 138 they are presented with a display screen which consists of five main information areas. FIG. 2 illustrates the display of the verification module for an exemplary incident, according to one embodiment of the present invention.
Incidents are queued to the verification station by incident number so that the oldest incident is always processed first. Many of the verification application screens are also used in later processing applications, that may include quality assurance, a hold queue, an interstate queue, Police authorization, and an offense viewer.
When the incident is first loaded, the display area 206 will display the plate zoom shot. The user may then select a command 208 to view the face zoom shot. When first displayed, the uncompressed images in TIFF format will be loaded from the file server using the images' stored address paths.
Note that after an incident has been verified, later processing steps that use these images will load a compressed JPEG version of the image that has been stored in the database. This technique generally improves the speed of the system and keeps database file sizes to a minimum, at the cost of some small loss of image quality after the verification stage.
To allow easier recognition in later processing steps, the areas of interest of both plate and face shot images can be magnified by the verification user. For this function, a zoom control is provided. This control allows the image to be enlarged, panned, and allows intensity and contrast adjustments. The zoom control for face shots has an additional mask function to allow masking the identity of any passengers in the vehicle for privacy reasons. The zoomed images are used for all processing steps after the verification step. Note that the primary evidence images are not modified, only the compressed JPEG images that are stored in the database are manipulated.
When the incident is first loaded, the main display area 212 of the verification screen area will display the “A” scene shot. The user may click on a button 218 to view the “B” shot. These images will be displayed in JPEG format and loaded directly from the database. The A shot is taken as the vehicle crosses the stop line and the B shot is taken after the vehicle enters the intersection. As illustrated in FIG. 2, the “B” scene shot is displayed.
In FIG. 2, display area 210 is the data block details area. This area displays a representation of the incident details as captured on site and the incident number allocated to the details at the time of insertion of the incidence into the database from the central processor. Each image captured by the system has a data bar 212 at the top of each image to provide an additional level of security. The information in the data block 210 must match the information in the data bar 212. This ensures that images have not been incorrectly assigned.
The image of FIG. 2 also includes a Motor Vehicles Department (DMV) details area 216. In this area the user types in the license plate details from the incident vehicle and executes a plate look-up from the DMV database. In general, the DMV lookup consists of a number of automatic steps, including looking up the registration number of the vehicle to return registered owner(s) details, looking up personal details of the driver to retrieve a driver's license number for the registered owner returned from the first lookup, and looking up the driver's license to return complete driver's license details.
Following a successful lookup, the DMV details area 216 of the verification screen of FIG. 2 will display some of the retrieved information. FIG. 7 illustrates the DMV details area in greater detail. The license plate and vehicle information is displayed in the top half of display area 700. The name and address of the driver, or company, if the vehicle is company-owned is displayed in display area 704, and the driver's license information for the driver is displayed in display area 706.
If any one of the steps of the DMV lookup is unsuccessful, a DMV lookup screen may be presented to the user. FIG. 8 illustrates a DMV lookup screen, according to one embodiment of the present invention. The DMV lookup screen 800 allows the user to execute each of three lookup steps incrementally. The user is able to enter the various items of information, such as the vehicle registration (license plate) number, personal details of the driver, or the driver's license number. The registration number of the vehicle is entered and displayed in display area 802, the vehicle details are entered and displayed in display area 804, and the driver details are entered and displayed in display area 806.
Use of the DMV lookup screen may be necessary in the event of multiple records being returned for either the registration number or the personal details lookups, i.e., if more than one owner was registered against the vehicle or if more than one person had the same name. The DMV lookup screen may also be used to modify user-defined search criteria in the event of returned owner records being flawed in some manner, such as if a “0” number was included in a name instead of an “O” letter.
The returned alleged offender details will be transferred to the relevant fields on the lower half of the DMV lookup screen 800 when the user clicks the ‘Accept’ button on the verification screen of FIG. 2. The user may execute multiple lookups if unsatisfied with the initial returned results. Each DMV lookup will be logged against a particular user and date/time stamped. The lookup log can be made viewable.
This area at the bottom right of the verification screen of FIG. 2 shows the buttons 220 corresponding to the different ways the incident can be processed by the user, i.e. how the status of the incident should be updated.
The user may click the ‘Hold’ button to put the incident “on hold” if there is not enough information to accept or reject the incident. To put an incident “on hold”, the user must also select the hold reason from a displayed hold reasons form. The most common reason to do this would be if the vehicle did not have an in-state registration. For this circumstance, an interstate lookup process might be implemented.
If the user decides the incident is not a valid offense, or for any other reason cannot be issued to an alleged offender, the incident can be rejected using the ‘Reject’ button. In this case, the user will be presented with a reject reasons form to select the reason in the same way as for hold reasons.
The user may decide to restart an incident, which would remove all zooming, masking, and also clear any DMV details that may have been returned. In the case of an incident being restarted, the history of the incident would reflect this and any DMV look-ups would also have been logged. The last option is to accept an incident as valid.
After one of the four choices has been selected, the next incident will be displayed and the process repeated. The user will have the ability to view an incident's history to date and add new comments to an incident.
In one embodiment of the present invention, the DMV lookup form 800 is also available from other applications. For example, the form may include an interstate queue application, so that when another state returns information on registration requests sent to it, the user can enter registration details against an incident. This area of the form may also be editable in the hold queue application when the incident is being ‘verified’ to extract name and address details from returned DMV registered owner data. It will generally not be editable in the hold queue application when the incident has already been verified, i.e., when the incident had been put on hold from the quality assurance module.
Quality Assurance Process
The data processing system 104 of FIG. 1A also includes a quality assurance (QA) module 140. In one embodiment, the QA module uses the same user interface as the verification module,. illustrated in FIG. 2. In the QA module, the user does not have any image editing facilities and may not change any of the vehicle or alleged offender details or execute a DMV look-up. All incidents that have a status of “Accepted by Verifier” or “Accepted by Hold Operator as Verifier” will be available for quality assurance. The system tracks users who are logged in to the QA module and will not queue any work to them that they have “verified”, be it at the verification application or hold queue application.
When a quality assurance session begins, the four images (plate, face, scene A, scene B) in compressed JPEG format are loaded from the database 136. The plate and face images displayed are those that were manipulated at the verification stage 138. Initially the scene A and zoomed plate shots are displayed. The data block details area is then populated, and the current incident status is displayed.
The user will assess the incident as presented, and may accept, reject or hold the incident. Acceptance updates the incident's status to that of “Accepted by Verifier and QA”. Rejecting the incidents results in the display of the reject reasons form. The user selects a reason and confirms to update the incident's status to that of “Killed” (rejected). The user will be logged as the QA operator of the incident. No further action will be taken with this incident.
If the user elects to hold, a hold reasons form is displayed, and the incident's status is updated to that of “Accepted by Verifier, On Hold by QA”. The user will be logged as the QA operator of the incident. As the incident was put on hold by QA, the system will flag this condition and prevent the incident from being editable at the hold queue application, i.e., only incidents that have been put on-hold from the verification application may be editable at the hold queue application. To be editable means to be able to manipulate the face and plate shots, execute a DMV lookup or to be able to edit an alleged offender's details on the DMV lookup screen.
In one embodiment of the present invention, the data processing system 104 includes a hold queue application. Incidents that may be valid but need further clarification are queued to this application. The application starts by displaying a hold queue main screen which shows a list of all incidents that are on hold that can be processed by the current user. The user may click on any listed item and then click an appropriate command to display the same screen as used in the verification application. Incidents may be put on hold by either the verification module 138 or the quality assurance module 140. When an issue has been resolved for an incident, the operator can then advance the incident by either accepting or rejecting it. If the incident was put on hold at the verification stage, then the hold operator becomes the effective verifier.
In one embodiment of the present invention, the data processing system also includes an interstate queue module. This module appears and operates in the same manner as the hold station that deals with other incidents put on-hold. For this application, a list of registrations can be printed to be faxed to another state registration authority, so that they can provide details by return of fax. This would normally be performed after entering. a search filter to list only incidents of one jurisdiction that have not been assessed. The user would then update an incident's details by finding the relevant incident. The incident may then be advanced to QA as normal.
Police Interface Modules
The traffic violation monitoring and reporting system 100 of FIG. 1A also includes an interface to one or more police departments 106. The data processing application 104 provides the police department 106 the ability to select one of three modules. These are a police authorization module, an offense viewer module, and a police report module.
An exemplary structure of the police authorization module's main screen interface screen is illustrated in FIG. 9A. Interface screen 900 provides a list 902 of incidences by date and time, with license plate numbers for the offending vehicles. All incidents having been accepted as valid by the verification and QA process will be presented on a list in (configurable) batches on the main screen of the police authorization application. Incidents will be listed for batch creation by their incident date and time, thereby the oldest will be presented the police first.
Appropriate police personnel will have the ability to view individual incident details by selecting them and clicking an appropriate command button, such as the ‘show details’ button 904. They will be presented with a non-editable screen, similar to the verification screen of FIG. 2. They may accept or reject a single incident from this screen. For data integrity, the police will not have the ability to put an incident on hold, or to view or enter comments.
The user (police personnel) will assess the incident and may decide to accept, reject or take no action by canceling from the incident. If the user decides to accept the incident, the incident status is updated to “Ready for Notice Processing” in the database 136 and the user is returned to the main list 902. If the user decides to reject the incident, the incident status is updated to “Killed” and the user is returned to the main list 902. The incident is logged in the database as having been rejected by police and the reason is recorded for reporting and auditing purposes. No further action will be taken with this incident. If the user decides to cancel, the incident status remains unchanged and the user is returned to the main list.
It may be possible for the authorizing officer to view each incident on the list and act on each one individually or they will at any stage return to the main list and decide to accept all the remaining incidents listed by selecting an ‘Accept All’ function.
Within the police authorization application, the offense viewer module displays incident images for incidents that have been confirmed as violations. This module will also be security protected and only police authorized personnel may access it. The user will use either a notice number, vehicle registration, or incident number as a search filter.
On entering a search parameter and executing a search, the system will display the four incident images, data block details, and DMV details. Additional searches can be performed from the main display in the same manner as the initial search.
The police reports module within the police authorization application allows reports to be run for police functions. The police can then use these reports to follow up on delinquent notices, and similar functions. The reports available are presented in a list and can be previewed through a police authorization application user interface.
The police authorization application can also include a delinquent notices report that lists delinquent reports in a list. An interface dialog can prompt the user for the number of days and then the report will be displayed. The report will include all notices for which payment is overdue by the selected number of days.
A dismissals report item can also be included in the police authorization application. This report lists all notices that have been cancelled because they were not processed within the time limits or because of a nomination. A nomination occurs when an alleged offender nominates another person as the driver at the time of the incident. In either case, a previously issued notice needs to be cancelled from the court records. This report can be used as a list to send to the court to request dismissal of cancelled notices.
The police authorization application also includes a notices module that allows the police department to issue and preview the Notices to Appear which are to be issued to the violators.
The traffic violation monitoring and reporting system 100 also includes a court interface module 110 that allows a user to communicate details of notices to the courts electronically, and subsequently receive updates on notice statuses from the courts. In one embodiment, this process is managed automatically using a third party scheduling program by executing database script files.
FIG. 9B illustrates the court interface screen generated by the court interface module 110, according to one embodiment of the present invention. Court interface screen 950 includes a display area 952 that lists the notices that have been approved and are ready to be sent to the alleged offenders. The court interface screen 952 also includes a display area 954 that allows access to files or documents received from the court. These may include acknowledged notices and disposition of notices processed by the court. A text display area 956 may be provided to display messages associated with any incidents listed in display area 952.
A manual court interface module can also be provided as a backup if the automatic system fails, or if unscheduled activities are required. The manual court interface module allows the following steps to be initiated: generate notice records from newly approved offense incidents, send details of new notices, receive acknowledgment (edit report) of sent files, and receive weekly dispositions. The database packages that are executed for each of these functions can either be initiated manually by clicking the interface selection, or automatically from a third party scheduling program by executing database script stored files. For every function, the details of the function are stored in a time-stamped record in log table with a unique session log id number. The number of records affected or any errors encountered is also stored.
In one embodiment of the present invention, the notice creation function is initiated either by a scheduler program or will occur automatically when the manual court interface screen is selected. Notice records are created by notice printing module 142 for incidents that have been authorized by the police. FIG. 10 is a flowchart that illustrates the steps of creating a notice, according to one embodiment of the present invention. In step 1002, all traffic incident records that have a status of ‘Ready for Notice Processing’ or ‘Ready for Warning Processing’ are identified.
For each incident that is found, a check is performed on the age of the incident, step 1004. If, in step 1006, it is determined that too much time has elapsed since the incident occurred, the incident be rejected on the grounds that it is too old to issue, step 1008. This typically occurs because, depending on the jurisdiction, notices must usually be sent to an alleged offender within specified period of time (e.g., 15 days) of the offense date, address details update date, or nomination date.
For each incident found that is within the allowed time period, an Offence Notice record is created and assigned a citation number, step 1010. The created notices will now have a status of ‘New’ if the status was ‘Ready for Notice Processing’, or ‘New Warning Letter’ if the status was ‘Ready for Warning Processing’. An associated offender and offender address record is created to store the personal details and address of the owner that was selected during the incident verification process.
After the appropriate notices have been created, the notices may be sent to court. This function can be initiated either by a scheduler program or manually by selecting a ‘Create Notices File’ selection on the court interface display screen 950. For this process, the system first searches for all notices with the appropriate status (e.g., New), and excludes all those that are too old. The details of the notices are written to a new export file (with a pre-defined name and location) in a format that is suitable for the court's system. Notices that are too old have their statuses updated to ‘Sent to Police for Dismissal’. The other notices will have their statuses updated to ‘Sent To Court’. The system may display a count of how many notices were updated to ‘Sent To Court’ and ‘Sent to Police for Dismissal’.
The export file created may have the text ‘EDIT ONLY’ in the header to indicate that the file is to be checked for syntax errors by the court system and that an edit report is to be produced by the court system to act as an acknowledgement of receipt. A procedure in the court system to process the file is to be initiated via a modem connection, which ma y be handled by a scheduler program or manually by an operator.
If the notice is to be issued to the violator by a third party, non-judicial or non-police agency, the court must acknowledge receipt of a notice before that party can print a hardcopy of it and mail it to alleged violator. The notice printing module of the data processing system 104 provides a user interface screen that lists and displays in preview form, notices that are to be printed. Such a notice preview form is illustrated in FIG. 11.
In one embodiment of the present invention, printing a notice involves several main steps. First, the current user is saved as the issue user in the notice record, and the notice status is updated to “Notice Printed” or “Warning Letter Printed”, as appropriate. Two scene images, a plate zoom image, a face zoom image, a police authorizer signature image, and the issue user's signature image files are copied from the database 136 into a data processing directory as graphic files (such as .jpg files).
Next, the document is previewed on the screen to ensure all images are retrieved, and then the document is printed to the printer. Note that a preview of a document that has not yet been printed may not display the details of the person issuing the notice because it has not yet been issued.
FIG. 11 illustrates a notice preview displayed in a user interface screen, according to one embodiment of the present invention. The following details appear on each Notice to Appear: the name and address of the alleged offender, details of the incidence, the four incident images as saved by the verification operator, the location of the incident, the time and date of incident, and fine payment information. Also included is a section where an alleged offender may complete details of the person that they may wish to nominate as the driver of the vehicle at the time, as well as information relating to what the alleged offender may do if he or she disagrees with the allegation. The notice may also include a scanned signature of the police officer that authorized the incident for issuing as an offense, and a scanned signature of the person that issued the notice, i.e. printed and posted the notice.
Depending upon the computer implementation, the report preview function may also allow the user to manipulate the notice file, such as print to the notice to a selected printer, or export the notice to an HTML or text file.
In one embodiment of the present invention, an alleged offender may claim they are innocent and subsequently nominate another driver. There are two methods whereby a person may do this. First, the Notice to Appear will have a section on it that the person may complete and return to the party that issued the notice, or the person may complete a Certificate of Innocence at a police station and the police will forward it to the issuing party.
The data provided by the traffic violation monitoring and reporting system constitutes legal evidence that can be used to convict a traffic offender for a traffic violation. In one embodiment of the present invention, the evidentiary package consists of a copy of the notice to appear, in addition to other documents, which are not necessarily produced by the system. Such documents could include information supplied by the court, a chain of evidence testifying as to the integrity of the image data, and a statement of technology.
Image Analysis Expert Systems
In one embodiment of the present invention, an image analysis system to automate components within the data processing system 104 is implemented. Image analysis is a process of discovering, identifying and understanding patterns that are relevant to the performance of an image-based task. One such task is the ability to automatically locate and read license plate information in evidentiary images. Here the pattern of interest is license plate shapes and alphanumeric characters. The goal of the image analysis is to automatically locate these objects and perform character recognition with the accuracy of a human operator.
The advantage of an image analysis system in the verification process of the data processing system would be that all vehicle, owner and incident details can be provided for visual verification at a first instance all complete and thus requiring little or no manual data entry.
The elements of image analysis can be categorized into three basic areas, low level processing, intermediate level processing, and high level processing. The categories form the basis of a framework in describing the various processes that are inherent components of an autonomous image analysis system.
Low level processing deals with the functions that may be viewed as automatic reactions that require no intelligence on the part of the image analysis system. This classification would encompass image compression and/or conversion such as the application of a standard set of filters for image processing.
Intermediate level processing deals with the task of extracting and characterizing components or regions in an image for low level processing. This classification encompasses image segmentation and description that is the isolation, extraction and categorizing of objects within an image.
High level processing involves the recognition and interpretation of the extracted objects. The application of intelligent behavior is most apparent in this level as it entails the capacity to learn from example and to generalize this knowledge so that it can be applied in new and different circumstances.
Image analysis systems utilizing Expert Systems technology, can be used to accurately identify, extract, and translate areas of interest imprinted or appearing in images recorded by the enforcement camera system 100 of FIG. 1A. In general, the technology requires the acquisition of knowledge through a process of extracting, structuring, and organizing knowledge from one source so it can be used in software. There are three main areas central to knowledge acquisition that requires consideration in the development of the image analysis expert system. First, the domain must be evaluated to determine if the type of knowledge in the domain is suitable for the image analysis expert system. Second, the source of expertise must be identified and evaluated to ensure that the specific level of knowledge required by the image analysis expert system is provided. Third, the specific knowledge acquisition techniques and participants need to be identified.
The objective of the image analysis expert system is to accurately identify, extract and translate optical data appearing in the photographic evidence captured by any type of enforcement camera systems.
Many film based camera systems optically imprint textual information of the offense onto each photograph. For example speed enforcement camera systems imprint onto each image; information such as measured speed and direction the offending vehicle was travelling, the speed zone and location the camera was monitoring, the operator ID supervising the deployment, and the time and date of the offense. The process can also be applied in the identification and extraction of license plate vehicle details that can be used to identify the offending vehicle owner.
The image analysis expert system knowledge base can be derived from a range of sources such as textbooks, manuals and simulation models, although the core knowledge is derived from human experts. The human experts themselves may not necessarily be a technical resource, but may include the operators or users of the system that make decisions based upon known business processes rather than technical issues. This type of inferred knowledge obtained indirectly by these experts does provide a useful resource for the knowledge base.
Knowledge acquisition embodies several processes and methodologies to capture, identify, and extract knowledge. Although fundamentally, knowledge is obtained from human experts which provides the static core or base line, the image analysis expert system can derive it's own dynamic knowledge by establishing trends or common themes, in essence drawn from it's own experience. The system achieves this ability through a unique feedback and tracking mechanism provided by the data processing system 104. The system has the ability to determine if the information provided is correctly within a relatively short time (in some cases instantly—using any inherent validating features that may be incorporated in the extract data such as a checksum).
However, with traditional expert systems, information derived is based on a conclusion made from a set of inputs with no mechanism validating the result, thus if the same inputs are feed into the expert systems the same conclusions are made. With either expert system, knowledge acquisition is typically achieved by observing an expert solve real problems, through discussions, by building scenarios with the expert that can be associated with different problem types, developing rules based on interviews and solving the problems with them, and other similar ways. In addition to these methods of knowledge acquisition, the image analysis expert system can also draw knowledge from inferred knowledge obtained by the verification and adjudication processes' audit trail, allowing more than one result for the same set of inputs, accessing external or other indirect sources of inputs available in the problem domain, and other similar methods.
The image analysis expert system and image computer are the primary components of the image processing system used in the traffic camera office system employing an automatic infringement processing system. The image computer provides the system with all the offense information in electronic form required in issuing an infringement notice.
For a speed infringement, the image processing system will provide two digital images of each offense, one a low-resolution version representative from a digital version of the original image, the other a high-resolution extraction of the license plate area only. In addition, textual offense details appearing in captured image is extracted using Optical Character Recognition (OCR) processes. Details of the OCR process used for the digital imaging process that extracts the license plate image are described in related U.S. patent application, Ser. No., 09/028,675, filed on Feb. 24, 1998 and entitled “Vehicle Imaging and Verification”, which is hereby incorporated by reference.
FIG. 5 illustrates a typical speed camera offense output provided by the image processing system, according to one embodiment of the present invention. In FIG. 5, the output screen 500 includes several different image areas. An image of the offense is displayed in display area 502. A close-up image of the license plate of the offending vehicle is shown in display area 504, and the details of the offense are displayed in display area 506. This information is validated and confirmed by two separate manual processes before the actual infringement is issued. A traffic camera office infringement processing system typically consists of a high-speed film scanner providing images for the image computer to process under the control of a file arbitrator. Infringement information is automatically extracted by the image computer and stored into a database for manual verification and adjudication at the verification station.
FIG. 12 illustrates the traffic camera office infringement processing system components, according to one embodiment of the present invention. Also illustrated in FIG. 12 are the components that are encompassed by the image processing system.
Raw digital images of the offenses either obtained directly from the field digital cameras or scanned 35 mm wet film converted into a digital form. The file arbitrator 1202 provides serialized access to the raw offense data. The image computer 1214 within the image processing system 1210 performs the primary image analysis tasks and is the primary interface between database 1208 and the raw digital images 1216. A verification station 1206 provides a mechanism of visual manual adjudication of actual offense and information provided by the image processing system 1210. If the information provided is correct and the offense complies with all appropriate business rules then the infringement is issued to the vehicle owner.
The supervisor station 1204 is used to validate any offense that may have been rejected during the verification and adjudication process of the traffic camera office business flow. Database 1208 may be a relational database, such as an Ingress™ Relational Database system running under a UNIX™ operating system under the HP-9000™ platform. It provides the central repository for all data including offense images and data, audit trail and archiving.
In one embodiment, the image analysis expert system 1220 provides the image processing system 1210 with human expert like behavior, thus endowing the image computer essentially with Artificial Intelligence to solve problems efficiently and effectively.
Regardless of enforcement type all infringement images are returned to the traffic camera office for processing including all the infringement details in an electronic form as well as a camera set-up and deployment log, which the operator is required to answer. The speed camera setup and deployment log contains useful information concerning the actual deployment conditions and environment, knowledge that can aid the image analysis process.
A file arbitrator 1202 detects the new image file, and initiates the image computer 1214 to start the image analysis process. The image computer then validates the image file, extracts from the file the area of the image bounding the data block (containing the offense details), segments and represents the characters within the data block, rebuilds missing or broken characters, and translates the character objects in the text by the process of OCR. Next, the license plate of the offending vehicle is searched. Once it is found, the area is extracted for OCR, the license plate details are determined, including jurisdiction. A low resolution JPEG compressed image representing the entire image is then produced, and a high resolution JPEG compressed image crop of the license plate area only is made. The image set and OCR text data is transferred to the database.
Once the data reaches the database, it is presented to the verification station for visual confirmation and adjudication by a trained operator. The normal process of the operator is to simply confirm the offense details automatically extracted by the image computer. Once these details have been confirmed, the vehicle owner details are searched and presented for content and syntax validation. Once the vehicle owner details are confirmed, the offense data is passed onto the quality system for inspection and issuing of an actual infringement notice.
Analyzing the process or work flow of the traffic camera office infringement processing system reveals several opportunities for the image analysis expert system to acquire and infer knowledge. From the beginning of the enforcement processing cycle, even before the film reaches the traffic camera office, the knowledge acquisition is occurring.
For instance, the speed camera setup and deployment log provide the image analysis expert system useful dynamic or temporary knowledge about the deployment configuration and environment that can be useful in the license plate extraction and OCR process. Information describing the weather condition, traffic direction and condition, the number of lanes monitored, and the lane the first few offending vehicles were traveling in, all provide useful information for the image processing system. Even though the acquired knowledge is stored temporarily (until the complete deployment has been successfully processed) archival information can also be created/updated about the camera and deployment location to help establish constants or trends (that is a site/camera profile).
Once the film data is stored into the main database, the image analysis expert system can access this data when each image computer starts processing a new image file. Since the first task of the image computer is to interpolate the data block area, the image analysis expert system can supply the imaging computer with the best data block location in the image. Accompanying this knowledge would also be the best extraction and OCR process to use (including the best performing parameters).
In the event that the processing scenario provided was unsuccessful, the image analysis expert system can provide information on alternative extraction and OCR processes. Both failures and successes are recorded by the image analysis expert system, improving the knowledge base, and hence the image processing performance and efficiency. Here the success and failure knowledge is known in real time with the aid of the check digit feature of the data block.
Next the image computer begins the license plate search and extraction process. Again the image analysis expert system can instruct the image computer to perform this process with the best performing algorithms and parameter scenario so far. Here the feedback of success or failure of the process is delayed as no automatic successful/failure mechanism exists (as with the data block check digit feature). Although the license plate location can be confirmed with the aid of the deployment log (for speed offenses) for at least the first few recorded offenses. Here the camera operator is required to record against each frame number which lane the offending vehicle was travelling.
However, until the offense is viewed at the verification station the actual image analysis performed by the image computer cannot be validated and hence the image analysis expert system cannot acquire the knowledge unless a verification priority is placed on the first few images of each new film or deployment.
The actual verification process can also influence the knowledge acquiring process of the image analysis expert system by prompting the verification operator with simple questions each time a correction is made to any part of the provided offense data. Alternative knowledge can be inferred by analyzing the corrections and business rule rejection to determine why the selected process for that particular infringement was unsuccessful.
FIG. 13 illustrates the functional components of the image analysis expert system 1220, according to one embodiment of the present invention. The acquiring module 1302 provides the knowledge database with real time knowledge deduced/provided by the image computer, inferred knowledge received directly from the verification station or analyzed from the system audit trail/system, or direct knowledge acquired from the traffic camera office infringement processing database.
The knowledge provider 1304 is the primary interface to the image computers, and provides the image computers with the necessary information and parameters to perform the required image processing tasks.
The local database 1306 serves as the central repository for all knowledge, performance statistics, short and long term data and configuration parameters for the image computers. The local database also serves as storage for neural network training set and template characters.
The knowledge graphical user interface (GUI) 1308 provides the user with the ability to display, modify, and delete the knowledge and database data. The knowledge GUI also allows the updating configuration parameters, character templates used by the OCR process and neural net training.
The image analysis expert system provides the image computer with a predefined scenario or collection of rules to follow to achieve a successful image analysis outcome. Unlike other Expert Systems, the combination of processing scenarios is relatively few since there is only a limited number of ways a data block of an offense image can be extracted. However, the image analysis expert system of the present invention is generally able to make adjustments to the parameters used by each process or rule, and therefore has an adaptive ability. This is achieved by deliberately varying these parameters and tracking or tracing the results through the system.
This mechanism of fine tuning the scenarios (or in some cases applying different scenarios all together) is called “sampling”. Sampling is a mechanism employed by the image analysis expert system to effectively perform tests by deliberately applying different image processing scenarios or parameter adjustments to improve the performance.
In one embodiment, this type of operation is performed at the beginning of a new deployment or film and randomly through each batch. The changes are tracked through the traffic camera office infringement processing system. Information on the success or failure is analyzed, allowing for real time fine-tuning of the system. Although the knowledge obtained may only be used on a temporary basis (that is only for the current batch), trends can be recorded and if need be the static knowledge can be upgraded.
In reference to the image processing system, a ‘scenario’ is a collection of image processing rules by which the image computer follows to produce a successful image analysis outcome. The mechanism by which these rules are stored and the knowledge endowed to the image computer depends on the level of sophistication employed by the image processing system.
Performance monitoring is a method of fine-tuning or detecting poor image analysis outcomes. The mechanism used is simply the correlation and analysis of statistics derived from real-time data allowing for the fine-tuning that may be required due to small differences or abnormal deployment conditions which were not catered for as part of the fundamental knowledge. Scenario statistics are a second type of statistical data that can be correlated based upon direct scenario outcomes and scenario variants with different parameter values.
A primary component of the knowledge acquiring module of the image analysis expert system is an expert system that infers knowledge from the verification station. Knowledge such as commonly made OCR mistakes (that is, characters which a regularly incorrectly recognized), invalid license plate selection, incorrect dynamic extraction thresh hold, and other such information is used in deducing as a result of sampling.
An important requirement of this module, particularly when tracing sampling mode images, is the correct identification of the image itself. A common theme or key must be employed by the verification module, audit system, database, image computer and image analysis expert sub-systems.
Access to main traffic camera office infringement processing database can provide indirect knowledge to the image analysis expert system that cannot be obtained directly from the images or verification process. For example, deployment log information and other additional film and location information provide useable knowledge for the image analysis expert system and image computers.
The core of the image analysis expert system contains all the image processing knowledge and image computer configurational/operational parameters. The local database encompasses both static and dynamic data. The structure of the database may vary depending on the form of the knowledge and data. Character templates and Neural Network training sets may also be stored on this database.
Although embodiments of the present invention have been described as deployed in traffic environments involving red light or stop sign offenses at intersections, it is to be noted that alternative embodiments can be deployed in other traffic environments. For example, the traffic violation monitoring and reporting system can be deployed and used along a stretch of road to determine if vehicles are speeding.
Moreover, embodiments may include facilities for issuing multiple offenses for a single incident. For example, a red light camera with speed tracking can detect and record a speeding vehicle running a red light. The multiple notice may be in the form of separate notices, one for the red light offense and one for the speeding offense, or one notice recording all offenses.
Embodiments of the present invention incorporate various methods to ensure the security and integrity of the digital images obtained at the target intersection. In one embodiment of the present invention, public key cryptography methods are utilized in the functionality of the digital camera imaging system. The original violation evidence is encrypted at the point of capture in the digital camera system 102 of FIG. 1A. As each pixel within the CCD is discharged outside the module, they are converted into a digital stream and encrypted in real time preserving its original raw form. Applying this process at this early stage eliminates the need for special purpose peripheral devices for the storage, transfer, and handling of data.
In one embodiment of the present invention, variations of known public-key and secret-key encryption systems are used to implement digital envelope cryptography for the digital traffic camera system. Each camera system is assigned a unique digital certificate that is recreated whenever there is any alteration to the system. The certificate nominates relevant system details including the camera's serial number and supplies an identifiable public key for the particular camera system. Later, this public key is used to identify the specific source for each set of evidence reaching the data processing system.
As each offense occurs, the camera system collects relevant evidence which is comprised of a number of elements or ‘properties’, including the various image files, the speed data, the time of offense and so on. The camera system then uses all the details of its current, unique digital certificate to build a hash function by applying recognized public key cryptography ‘hashing’ algorithms. The hash function is a one-way equation that is used to ‘sign’ each property of the offense as it occurs with its own, unique digital signature.
The camera system then places each of the signed properties for an offense into an offense database and places this in the system's server outbox (using, for example, the Microsoft™ Message Queue server outbox). The outbox server then breaks all the information in the offense database into smaller, more easily transportable packets, or ‘mini-envelopes’, of information. It then applies another unique digital signature to each packet (using the public key techniques above).
Where there are remote communications such as telephone, ISDN, fiber optic, and so on, between the camera site and the data processing system, the signed packets can be electronically transferred over the Internet for processing using a Virtual Private Network. In one embodiment, the data processing system server secures the transmission process by using IP SEC, a standard Internet protocol that is widely used to protect electronic transmissions over unprotected public networks.
Where there is no remote communication to the camera site, the signed packets may be either downloaded to removable media (e.g., disks), for physical transport to the data processing system, or downloaded to a camera operator's mobile computer for transfer to the system.
Each signed packet is received at the data processing system by the data processing system's outbox server, which decrypts the mini-envelope packets and automatically checks the authenticity of their signatures. The original offense database is then reassembled from its various signed properties to recreate the original offense file.
The unique digital signature on each property is then authenticated to identify the source of the property (thus defining the camera that originally captured the evidence), and verify the integrity of that property (by confirming that its original digital signature is intact and unaltered). The original properties with their intact, authenticated digital signatures are then stored as the original database (i.e., primary evidence) for the offense.
The data processing system then selects the data and image items required for citation processing, copies these, and works on the duplicates. The original files with their intact, authenticated, digital signatures are stored separately as the protected primary evidence for the offense. From then, every access or attempted access is logged to an audit chain so the life of the offense is completely accountable.
Any files with scrambled signatures alerting corruption or alteration of evidence are not sent for processing. Processing can only proceed on evidence that has been confirmed as authentic. Such an encryption and authorization system is useful for deployment in jurisdictions that allow the introduction of digital evidence.
The application of digital signatures for traffic law enforcement for the purposes of offense authentication provides for a method of securing data integrity that is independent of the media that it is stored and/or transmitted on. The process provides for mechanism of identifying the capture source (that is the camera system) and legitimacy.
As illustrated in the figures of the present application and described herein, aspects of the present invention may be implemented on one or more computers executing software instructions. According to one embodiment of the present invention, server and client computer systems transmit and receive data over a computer network or standard telephone line. The steps of accessing, downloading, and manipulating the data, as well as other aspects of the present invention are implemented by central processing units (CPU) in the server and client computers executing sequences of instructions stored in a memory. The memory may be a random access memory (RAM), read-only memory (ROM), a persistent store, such as a mass storage device, or any combination of these devices. Execution of the sequences of instructions causes the CPU to perform steps according to embodiments of the present invention.
The instructions may be loaded into the memory of the server or client computers from a storage device or from one or more other computer systems over a network connection. For example, a client computer may transmit a sequence of instructions to the server computer in response to a message transmitted to the client over a network by the server. As the server receives the instructions over the network connection, it stores the instructions in memory. The server may store the instructions for later execution, or it may execute the instructions as they arrive over the network connection. In some cases, the downloaded instructions may be directly supported by the CPU. In other cases, the instructions may not be directly executable by the CPU, and may instead be executed by an interpreter that interprets the instructions. In other embodiments, hardwired circuitry may be used in place of, or in combination with, software instructions to implement the present invention. Thus, the present invention is not limited to any specific combination of hardware circuitry and software, nor to any particular source for the instructions executed by the server or client computers.
In the foregoing, a system has been described for automatically monitoring and reporting instances of traffic violations. Although the present invention has been described with reference to specific exemplary embodiments, it will be evident that various modifications and changes may be made to these embodiments without departing from the broader spirit and scope of the invention as set forth in the claims. Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense.
|Cited Patent||Filing date||Publication date||Applicant||Title|
|US4847772||Feb 17, 1987||Jul 11, 1989||Regents Of The University Of Minnesota||Vehicle detection through image processing for traffic surveillance and control|
|US5191413||Nov 1, 1990||Mar 2, 1993||International Business Machines||System and method for eliminating interlace motion artifacts in captured digital video data|
|US5296852 *||Feb 27, 1991||Mar 22, 1994||Rathi Rajendra P||Method and apparatus for monitoring traffic flow|
|US5381155 *||Jun 9, 1994||Jan 10, 1995||Gerber; Eliot S.||Vehicle speeding detection and identification|
|US5432547 *||Nov 16, 1992||Jul 11, 1995||Matsushita Electric Industrial Co., Ltd.||Device for monitoring disregard of a traffic signal|
|US5568406||Dec 1, 1995||Oct 22, 1996||Gerber; Eliot S.||Stolen car detection system and method|
|US5809161 *||Mar 22, 1993||Sep 15, 1998||Commonwealth Scientific And Industrial Research Organisation||Vehicle monitoring system|
|US5948038 *||Jul 31, 1996||Sep 7, 1999||American Traffic Systems, Inc.||Traffic violation processing system|
|US6038337 *||Mar 29, 1996||Mar 14, 2000||Nec Research Institute, Inc.||Method and apparatus for object recognition|
|US6111523 *||Nov 20, 1995||Aug 29, 2000||American Traffic Systems, Inc.||Method and apparatus for photographing traffic in an intersection|
|GB2272305A *||Title not available|
|WO1997004417A1||Jul 12, 1996||Feb 6, 1997||The Victoria University Of Manchester||Image enhancement|
|1||*||Lawson, S.D., et al., "Red-light running and surveillance cameras-policy issues related to accident reduction and enforcement", Road Traffic Monitoring (IEE Conf. Pub 355), 1992.*|
|2||*||Lewis, "Future system specifications for traffic enforcement equipment", IEEE Colloquium on Camera Enforcement of Traffic Regulations, Nov. 1996.|
|Citing Patent||Filing date||Publication date||Applicant||Title|
|US6681195 *||Mar 19, 2001||Jan 20, 2004||Laser Technology, Inc.||Compact speed measurement system with onsite digital image capture, processing, and portable display|
|US6728623 *||Nov 5, 2002||Apr 27, 2004||Hitachi, Ltd.||Running control device for a vehicle|
|US6754663 *||Nov 22, 1999||Jun 22, 2004||Nestor, Inc.||Video-file based citation generation system for traffic light violations|
|US6894717 *||Jun 4, 2002||May 17, 2005||Charles Adams Bakewell||Mobile enforcement platform and aimable violation detection and documentation system for multiple types of traffic violations across all lanes in moving traffic supporting immediate or delayed citation generation as well as homeland security monitoring activities|
|US6959499 *||Feb 20, 2004||Nov 1, 2005||Vincent Patrick Bini||Device and method for measuring the length of a fish|
|US6970102 *||May 5, 2003||Nov 29, 2005||Transol Pty Ltd||Traffic violation detection, recording and evidence processing system|
|US6985827 *||Nov 18, 2003||Jan 10, 2006||Laser Technology, Inc.||Speed measurement system with onsite digital image capture and processing for use in stop sign enforcement|
|US7023475 *||Apr 8, 2002||Apr 4, 2006||Hewlett-Packard Development Company, L.P.||System and method for identifying an object with captured images|
|US7188307 *||Nov 8, 2001||Mar 6, 2007||Canon Kabushiki Kaisha||Access system|
|US7283672 *||Jun 29, 2001||Oct 16, 2007||Harris Scott C||Image compression by object segregation|
|US7323987||Jun 10, 2005||Jan 29, 2008||Sigma Space Corporation||Compact single lens laser system for object/vehicle presence and speed determination|
|US7433764 *||Apr 15, 2003||Oct 7, 2008||Gatsometer B.V.||Method and system for recording a traffic violation committed by a vehicle|
|US7495579||Jun 13, 2005||Feb 24, 2009||Sirota J Marcos||Traffic light status remote sensor system|
|US7509683 *||Aug 26, 2002||Mar 24, 2009||Hewlett-Packard Development Company, L.P.||System and method for authenticating digital content|
|US7561183 *||Oct 8, 2002||Jul 14, 2009||Unisys Corporation||Mobile issuance of official documents with biometric information encoded thereon|
|US7599559||May 13, 2005||Oct 6, 2009||Color Savvy Systems Limited||Method for collecting data for color measurements from a digital electronic image capturing device or system|
|US7616293||Apr 29, 2005||Nov 10, 2009||Sigma Space Corporation||System and method for traffic monitoring, speed determination, and traffic light violation detection and recording|
|US7630556||Mar 12, 2007||Dec 8, 2009||Harris Technology, Llc||Image compression by object segregation|
|US7630557||Mar 12, 2007||Dec 8, 2009||Harris Technology, Llc||Image compression by object segregation|
|US7680545 *||Mar 3, 2006||Mar 16, 2010||Rudiger Heinz Gebert||System and method for speed measurement verification|
|US7688224||Oct 12, 2004||Mar 30, 2010||Siemens Industry, Inc.||Method and system for collecting traffic data, monitoring traffic, and automated enforcement at a centralized station|
|US7711738 *||Nov 15, 2000||May 4, 2010||West Services, Inc.||Method, system and computer-readable medium for accessing and retrieving court records, items and documents|
|US7751653 *||Aug 11, 2005||Jul 6, 2010||Color Savvy Systems Limited||Method for collecting data for color measurements from a digital electronic image capturing device or system|
|US7791501||Jun 2, 2008||Sep 7, 2010||Edward D. Ioli Trust||Vehicle identification, tracking and parking enforcement system|
|US7812711 *||Jun 28, 2007||Oct 12, 2010||Alertstar Safety Corporation Usa||Passenger vehicle safety and monitoring system and method|
|US7856402 *||Apr 7, 2008||Dec 21, 2010||United Services Automobile Association (Usaa)||Video financial deposit|
|US7869621 *||Jun 7, 2007||Jan 11, 2011||Aydin Arpa||Method and apparatus for interpreting images in temporal or spatial domains|
|US7882199 *||Feb 27, 2001||Feb 1, 2011||Sony Corporation||System and method for effectively implementing an electronic image manager device|
|US7893846||Jan 25, 2010||Feb 22, 2011||Siemens Industry, Inc.||Method and system for collecting traffic data, monitoring traffic, and automated enforcement at a centralized station|
|US7894632 *||Sep 20, 2007||Feb 22, 2011||Electronics And Telecommunications Research Institute||Apparatus and method of estimating center line of intersection|
|US7907780||Aug 19, 2009||Mar 15, 2011||Color Savvy Systems Limited||Method for collecting data for color measurements from a digital electronic image capturing device or system|
|US7912785||Apr 7, 2008||Mar 22, 2011||United Services Automobile Association (Usaa)||Video financial deposit|
|US7920251 *||Sep 23, 2008||Apr 5, 2011||Laser Technology, Inc.||Integrated still image, motion video and speed measurement system|
|US7920716 *||Dec 23, 2004||Apr 5, 2011||Fujitsu Limited||Vehicle search system and vehicle search method|
|US7920959||Apr 28, 2006||Apr 5, 2011||Christopher Reed Williams||Method and apparatus for estimating the velocity vector of multiple vehicles on non-level and curved roads using a single camera|
|US7974466||Nov 23, 2005||Jul 5, 2011||Color Savvy Systems Limited||Method for deriving consistent, repeatable color measurements from data provided by a digital imaging device|
|US7986339||Jun 12, 2003||Jul 26, 2011||Redflex Traffic Systems Pty Ltd||Automated traffic violation monitoring and reporting system with combined video and still-image data|
|US8064706||Nov 21, 2009||Nov 22, 2011||Rpx Corporation||Image compression by object segregation|
|US8120513||Sep 3, 2010||Feb 21, 2012||Ioli Edward D||Vehicle identification, tracking and enforcement system|
|US8184863 *||Jul 7, 2011||May 22, 2012||American Traffic Solutions, Inc.||Video speed detection system|
|US8213685 *||Jan 7, 2008||Jul 3, 2012||American Traffic Solutions, Inc.||Video speed detection system|
|US8284996||Dec 17, 2009||Oct 9, 2012||Automated Speed Technologies, LLC||Multiple object speed tracking system|
|US8290237||Oct 31, 2007||Oct 16, 2012||United Services Automobile Association (Usaa)||Systems and methods to use a digital camera to remotely deposit a negotiable instrument|
|US8310377||Aug 24, 2009||Nov 13, 2012||Optotraffic, Llc||Mobile automated system for traffic monitoring|
|US8320657||Oct 31, 2007||Nov 27, 2012||United Services Automobile Association (Usaa)||Systems and methods to use a digital camera to remotely deposit a negotiable instrument|
|US8320663||Jul 2, 2010||Nov 27, 2012||Color Savvy Systems Limited|
|US8344909||Jan 14, 2011||Jan 1, 2013||Siemens Industry, Inc.||Method and system for collecting traffic data, monitoring traffic, and automated enforcement at a centralized station|
|US8351677||Oct 31, 2006||Jan 8, 2013||United Services Automobile Association (Usaa)||Systems and methods for remote deposit of checks|
|US8351678||Jun 11, 2008||Jan 8, 2013||United Services Automobile Association (Usaa)||Duplicate check detection|
|US8358826||Oct 23, 2007||Jan 22, 2013||United Services Automobile Association (Usaa)||Systems and methods for receiving and orienting an image of one or more checks|
|US8363899 *||Oct 12, 2009||Jan 29, 2013||Rent A Toll, Ltd.||Method and system for processing vehicular violations|
|US8374909||May 8, 2009||Feb 12, 2013||Rent A Toll, Ltd.||System, method and computer readable medium for billing based on a duration of a service period|
|US8391599||Oct 17, 2008||Mar 5, 2013||United Services Automobile Association (Usaa)||Systems and methods for adaptive binarization of an image|
|US8392332||Dec 8, 2010||Mar 5, 2013||United Services Automobile Association (Usaa)||Systems and methods for remote deposit of checks|
|US8422758||Sep 2, 2008||Apr 16, 2013||United Services Automobile Association (Usaa)||Systems and methods of check re-presentment deterrent|
|US8433127||May 10, 2007||Apr 30, 2013||United Services Automobile Association (Usaa)||Systems and methods for real-time validation of check image quality|
|US8452689||Feb 18, 2009||May 28, 2013||United Services Automobile Association (Usaa)||Systems and methods of check detection|
|US8464933||Jan 31, 2011||Jun 18, 2013||United Services Automobile Association (Usaa)||Systems, methods and apparatus for receiving images of one or more checks|
|US8473332||Nov 19, 2007||Jun 25, 2013||Rent A Toll, Ltd.||Toll fee system and method|
|US8473333||May 8, 2009||Jun 25, 2013||Rent A Toll, Ltd.||Toll fee system and method|
|US8494159 *||Apr 4, 2008||Jul 23, 2013||International Business Machines Corporation||System and practice for surveillance privacy-protection certification and registration|
|US8503972||Oct 30, 2009||Aug 6, 2013||Digital Ally, Inc.||Multi-functional remote monitoring system|
|US8520069||Aug 10, 2008||Aug 27, 2013||Digital Ally, Inc.||Vehicle-mounted video system with distributed processing|
|US8531520 *||Apr 5, 2002||Sep 10, 2013||Siemens Industry, Inc.||System and method for traffic monitoring|
|US8538124||May 10, 2007||Sep 17, 2013||United Services Auto Association (USAA)||Systems and methods for real-time validation of check image quality|
|US8542921||Jul 27, 2009||Sep 24, 2013||United Services Automobile Association (Usaa)||Systems and methods for remote deposit of negotiable instrument using brightness correction|
|US8598977 *||Apr 15, 2011||Dec 3, 2013||Tiny Towne International Llc||System and method for driver training in a controlled driving environment|
|US8600116 *||May 22, 2012||Dec 3, 2013||American Traffic Solutions, Inc.||Video speed detection system|
|US8611635||Dec 20, 2012||Dec 17, 2013||United Services Automobile Association (Usaa)||Duplicate check detection|
|US8688579||Jun 8, 2011||Apr 1, 2014||United Services Automobile Association (Usaa)||Automatic remote deposit image preparation apparatuses, methods and systems|
|US8699779||Aug 28, 2009||Apr 15, 2014||United Services Automobile Association (Usaa)||Systems and methods for alignment of check during mobile deposit|
|US8708227||Oct 31, 2006||Apr 29, 2014||United Services Automobile Association (Usaa)||Systems and methods for remote deposit of checks|
|US8738525||Dec 14, 2012||May 27, 2014||Rent A Toll, Ltd.||Method and system for processing vehicular violations|
|US8744905||Apr 30, 2009||Jun 3, 2014||Rent A Toll, Ltd.||System, method and computer readable medium for billing tolls|
|US8760318 *||Dec 6, 2011||Jun 24, 2014||Optotraffic, Llc||Method for traffic monitoring and secure processing of traffic violations|
|US8768753||Sep 6, 2006||Jul 1, 2014||Rent A Toll, Ltd.||System, method and computer readable medium for billing tolls|
|US8768754||Dec 22, 2006||Jul 1, 2014||Rent-A-Toll, Ltd.||Billing a rented third party transport including an on-board unit|
|US8799147||Oct 31, 2006||Aug 5, 2014||United Services Automobile Association (Usaa)||Systems and methods for remote deposit of negotiable instruments with non-payee institutions|
|US8823798 *||Dec 23, 2011||Sep 2, 2014||Xerox Corporation||Obscuring identification information in an image of a vehicle|
|US8837806||Jun 8, 2011||Sep 16, 2014||United Services Automobile Association (Usaa)||Remote deposit image inspection apparatuses, methods and systems|
|US8937559||Dec 15, 2011||Jan 20, 2015||Edward D. Ioli Trust||Vehicle identification, tracking and enforcement system|
|US8953044 *||Oct 5, 2011||Feb 10, 2015||Xerox Corporation||Multi-resolution video analysis and key feature preserving video reduction strategy for (real-time) vehicle tracking and speed enforcement systems|
|US8959033||Mar 15, 2007||Feb 17, 2015||United Services Automobile Association (Usaa)||Systems and methods for verification of remotely deposited checks|
|US8977571||Aug 21, 2009||Mar 10, 2015||United Services Automobile Association (Usaa)||Systems and methods for image monitoring of check during mobile deposit|
|US9002068 *||Nov 1, 2013||Apr 7, 2015||American Traffic Solutions, Inc.||Video speed detection system|
|US9019380 *||May 16, 2012||Apr 28, 2015||United Parcel Service Of America, Inc.||Detection of traffic violations|
|US9042647 *||Jun 6, 2013||May 26, 2015||Xerox Corporation||Adaptive character segmentation method and system for automated license plate recognition|
|US9129340||Dec 30, 2010||Sep 8, 2015||United Services Automobile Association (Usaa)||Apparatuses, methods and systems for remote deposit capture with enhanced image detection|
|US9158966||Oct 17, 2013||Oct 13, 2015||United Services Automobile Association||Character count determination for a digital image|
|US9171461 *||Mar 7, 2013||Oct 27, 2015||Steve Dabell||Method and apparatus for providing estimated patrol properties and historic patrol records|
|US9177197||Oct 16, 2014||Nov 3, 2015||United Services Automobile Association (Usaa)||Systems and methods for alignment of check during mobile deposit|
|US9177198||Oct 16, 2014||Nov 3, 2015||United Services Automobile Association (Usaa)||Systems and methods for alignment of check during mobile deposit|
|US9224136||Mar 20, 2014||Dec 29, 2015||United Services Automobile Association (Usaa)||Systems and methods for remote deposit of checks|
|US9253452||Aug 14, 2013||Feb 2, 2016||Digital Ally, Inc.||Computer program, method, and system for managing multiple data recording devices|
|US9262914||Sep 7, 2012||Feb 16, 2016||Tata Consultancy Services Limited||Rogue vehicle detection|
|US9286514||Oct 17, 2013||Mar 15, 2016||United Services Automobile Association (Usaa)||Character count determination for a digital image|
|US9336517||Oct 16, 2014||May 10, 2016||United Services Automobile Association (Usaa)||Systems and methods for alignment of check during mobile deposit|
|US9418487||Jan 31, 2014||Aug 16, 2016||Ats Tolling Llc||Billing a rented third party transport including an on-board unit|
|US9471838||Sep 5, 2012||Oct 18, 2016||Motorola Solutions, Inc.||Method, apparatus and system for performing facial recognition|
|US9569756||Jun 20, 2013||Feb 14, 2017||United Services Automobile Association (Usaa)||Systems and methods for image monitoring of check during mobile deposit|
|US20020054067 *||Feb 27, 2001||May 9, 2002||Ludtke Harold A.||System and method for effectively implementing an electronic image manager device|
|US20020055957 *||Nov 8, 2001||May 9, 2002||Hiroyuki Ohsawa||Access system|
|US20020186297 *||Jun 4, 2002||Dec 12, 2002||Bakewell Charles Adams||Mobile enforcement platform and aimable violation detection and documentation system for multiple types of traffic violations across all lanes in moving traffic supporting immediate or delayed citation generation as well as homeland security monitoring activities|
|US20030142339 *||Jan 17, 2003||Jul 31, 2003||Hewlett Packard Company||Discounted printing|
|US20030189499 *||Apr 5, 2002||Oct 9, 2003||Precision Traffic Systems, Inc.||System and method for traffic monitoring|
|US20030189646 *||Apr 8, 2002||Oct 9, 2003||Bean Heather Noel||System and method for identifying an object with captured images|
|US20040005141 *||Jun 25, 2003||Jan 8, 2004||Combs Robert G.||Data logging and digital video recording/playback system|
|US20040039929 *||Aug 26, 2002||Feb 26, 2004||Jerry Decime||System and method for authenticating digital content|
|US20040101166 *||Nov 18, 2003||May 27, 2004||Williams David W.||Speed measurement system with onsite digital image capture and processing for use in stop sign enforcement|
|US20040163267 *||Feb 20, 2004||Aug 26, 2004||Bini Vincent Patrick||Device and method for measuring the length of a fish|
|US20040222904 *||May 5, 2003||Nov 11, 2004||Transol Pty Ltd||Traffic violation detection, recording and evidence processing system|
|US20050002585 *||Jun 4, 2002||Jan 6, 2005||Michael Brauckmann||Privacy filter|
|US20050122235 *||Oct 12, 2004||Jun 9, 2005||Precision Traffic Systems, Inc.|
|US20050196016 *||Dec 23, 2004||Sep 8, 2005||Fujitsu Limited||Vehicle search system and vehicle search method|
|US20050206728 *||Apr 15, 2003||Sep 22, 2005||Janssen Theodorus M||Method and device for controlling a red light camera|
|US20050242306 *||Apr 29, 2005||Nov 3, 2005||Sirota J M||System and method for traffic monitoring, speed determination, and traffic light violation detection and recording|
|US20050285738 *||Jun 10, 2005||Dec 29, 2005||Antonios Seas||Compact single lens laser system for object/vehicle presence and speed determination|
|US20060020630 *||Jun 6, 2005||Jan 26, 2006||Stager Reed R||Facial database methods and systems|
|US20060047371 *||Apr 15, 2003||Mar 2, 2006||Gatsometer B.V.||Method and system for recording a traffic violation committed by a vehicle|
|US20060078199 *||Aug 11, 2005||Apr 13, 2006||Bodnar Gary N|
|US20060078225 *||May 13, 2005||Apr 13, 2006||Pearson Christopher H|
|US20060269104 *||May 3, 2004||Nov 30, 2006||Transol Pty, Ltd.||Traffic violation detection, recording and evidence processing system|
|US20070008176 *||Jun 13, 2005||Jan 11, 2007||Sirota J M||Traffic light status remote sensor system|
|US20070124198 *||Sep 6, 2006||May 31, 2007||Robinson Benjamin P||System, method and computer readable medium for billing tolls|
|US20070180840 *||Feb 9, 2006||Aug 9, 2007||Shostack Ronald N||Vertical self-contained air conditioner|
|US20070192177 *||Dec 22, 2006||Aug 16, 2007||Rent-A-Toll, Ltd.||Billing a rented third party transport including an on-board unit|
|US20070296817 *||Jul 7, 2005||Dec 27, 2007||Touradj Ebrahimi||Smart Video Surveillance System Ensuring Privacy|
|US20080048886 *||Jun 28, 2007||Feb 28, 2008||Brown Mark R||Passenger vehicle safety and monitoring system and method|
|US20080077312 *||Sep 26, 2006||Mar 27, 2008||Mrotek Charles G||System, Method, and Apparatus, for Mobile Radar Assisted Traffic Enforcement|
|US20080120172 *||Nov 19, 2007||May 22, 2008||Robinson Benjamin P||Toll fee system and method|
|US20080123902 *||Sep 20, 2007||May 29, 2008||Jeong-Ho Park||Apparatus and method of estimating center line of intersection|
|US20080154671 *||Mar 14, 2006||Jun 26, 2008||Delk Louis D||Emissions Tracking, Such as Vehicle Emissions Tracking, and Associated Systems and Methods|
|US20080166023 *||Jan 7, 2008||Jul 10, 2008||Jigang Wang||Video speed detection system|
|US20080231470 *||Jun 2, 2008||Sep 25, 2008||Ioli Edward D||Vehicle Identification, Tracking and Parking Enforcement System|
|US20080266140 *||Mar 3, 2006||Oct 30, 2008||Rudiger Heinz Gebert||System and Method For Speed Measurement Verification|
|US20080306868 *||Jun 7, 2007||Dec 11, 2008||Rent-A-Toll, Ltd.||Unlimited toll utilization|
|US20090002491 *||Aug 10, 2008||Jan 1, 2009||Haler Robert D||Vehicle-mounted video system with distributed processing|
|US20090079960 *||Sep 23, 2008||Mar 26, 2009||Laser Technology, Inc.||Integrated still image, motion video and speed measurement system|
|US20090175507 *||Oct 8, 2002||Jul 9, 2009||Schaffner Edwin K||Mobile issuance of official documents with biometric information encoded thereon|
|US20090222331 *||May 8, 2009||Sep 3, 2009||Robinson Benjamin P||System, method and computer readable medium for billing based on a duration of a service period|
|US20090228350 *||May 8, 2009||Sep 10, 2009||Robinson Benjamin P||Toll fee system and method|
|US20090244293 *||Jun 10, 2009||Oct 1, 2009||Schaffner Edwin K||Mobile issuance of official documents with biometric information encoded thereon|
|US20090251545 *||Nov 7, 2008||Oct 8, 2009||Shekarri Nache D||Systems And Methods For Incident Recording|
|US20100021054 *||Aug 19, 2009||Jan 28, 2010||Color Saavy Systems Limited|
|US20100111423 *||Oct 12, 2009||May 6, 2010||Balachandran Sarath K||Method and system for processing vehicular violations|
|US20100114675 *||Apr 30, 2009||May 6, 2010||Robinson Benjamin P||System, method and computer readable medium for billing tolls|
|US20100117865 *||Jan 25, 2010||May 13, 2010||Siemens Industry, Inc.||Method and System for Collecting Traffice Data, Monitoring Traffic, and Automated Enforcement at a Centralized Station|
|US20100149334 *||Dec 15, 2009||Jun 17, 2010||Jon Wirsz||Fixed and mobile video traffic enforcement|
|US20100167687 *||Oct 30, 2009||Jul 1, 2010||Digital Ally, Inc.||Multi-functional remote monitoring system|
|US20100172543 *||Dec 17, 2009||Jul 8, 2010||Winkler Thomas D||Multiple object speed tracking system|
|US20100284567 *||Apr 4, 2008||Nov 11, 2010||Arun Hampapur||System and practice for surveillance privacy-protection certification and registration|
|US20100328463 *||Sep 17, 2010||Dec 30, 2010||Digital Ally, Inc.||Rear view mirror with integrated video system|
|US20110043381 *||Aug 24, 2009||Feb 24, 2011||Sigma Space Corporation||Mobile automated system for trafic monitoring|
|US20110080495 *||Oct 1, 2010||Apr 7, 2011||Silicon Micro Sensors Gmbh||Method and camera system for the generation of images for the transmission to an external control unit|
|US20110109479 *||Jan 14, 2011||May 12, 2011||Siemens Industry, Inc.||Method and System for Collecting Traffice Data, Monitoring Traffic, and Automated Enforcement at a Centralized Station|
|US20110254655 *||Apr 15, 2011||Oct 20, 2011||Pierre Maalouf||System & Method For Driver Training In A Controlled Driving Environment|
|US20110267460 *||Jul 7, 2011||Nov 3, 2011||American Traffic Solutions, Inc.||Video speed detection system|
|US20120229627 *||May 22, 2012||Sep 13, 2012||American Traffic Solutions, Inc.||Video speed detection system|
|US20120286988 *||May 13, 2012||Nov 15, 2012||GSN-Pars Co.||Method and system for detecting moving vehicle speed through athird generation photo radar|
|US20120307064 *||May 16, 2012||Dec 6, 2012||United Parcel Service Of America, Inc.||Detection of traffic violations|
|US20130088600 *||Oct 5, 2011||Apr 11, 2013||Xerox Corporation||Multi-resolution video analysis and key feature preserving video reduction strategy for (real-time) vehicle tracking and speed enforcement systems|
|US20130093886 *||Oct 18, 2011||Apr 18, 2013||Ariel Inventions, Llc||Method and system for using a vehicle-based digital imagery system to identify another vehicle|
|US20130141253 *||Dec 6, 2011||Jun 6, 2013||Sigma Space Corporation||Method for traffic monitoring and secure processing of trafic violations|
|US20130162817 *||Dec 23, 2011||Jun 27, 2013||Xerox Corporation||Obscuring identification information in an image of a vehicle|
|US20130170711 *||Jan 4, 2013||Jul 4, 2013||Cyclops Technologies, Inc.||Edge detection image capture and recognition system|
|US20140126780 *||Nov 1, 2013||May 8, 2014||American Traffic Solutions, Inc.||Video speed detection system|
|US20140363052 *||Jun 6, 2013||Dec 11, 2014||Xerox Corporation||Adaptive character segmentation method and system for automated license plate recognition|
|US20140369566 *||Feb 21, 2013||Dec 18, 2014||Cyclops Technologies, Inc.||Perimeter Image Capture and Recognition System|
|US20140369567 *||Feb 21, 2013||Dec 18, 2014||Cyclops Technologies, Inc.||Authorized Access Using Image Capture and Recognition System|
|US20150199806 *||Jul 18, 2014||Jul 16, 2015||Industrial Technology Research Institute||Apparatus and method for vehicle positioning|
|WO2008151372A1 *||Jun 12, 2008||Dec 18, 2008||Andrew Lewis Christie||Traffic monitoring systems|
|WO2009126120A1 *||Apr 7, 2008||Oct 15, 2009||Wall Henry H||Traffic signal light control system and method|
|U.S. Classification||382/104, 701/119|
|International Classification||G08G1/123, G08G1/017|
|Cooperative Classification||G08G1/20, G08G1/0175|
|European Classification||G08G1/20, G08G1/017A|
|Sep 8, 2000||AS||Assignment|
Owner name: REDFLEX TRAFFIC SYSTEMS, AUSTRALIA
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:CIOLLI, ROBERT;WHYTE, PETER;ERCAN, GURCHAN;AND OTHERS;REEL/FRAME:011076/0807;SIGNING DATES FROM 20000522 TO 20000531
|May 27, 2003||AS||Assignment|
Owner name: NATIONAL AUSTRALIA BANK LIMITED, AUSTRALIA
Free format text: SECURITY AGREEMENT;ASSIGNOR:REDFLEX TRAFFIC SYSTEMS PTY LTD.;REEL/FRAME:014446/0238
Effective date: 20030512
Owner name: NATIONAL AUSTRALIA BANK LIMITED, AUSTRALIA
Free format text: SECURITY INTEREST;ASSIGNOR:REDFLEX TRAFFIC SYSTEMS PTY LTD.;REEL/FRAME:015017/0957
Effective date: 20030512
|Aug 16, 2004||AS||Assignment|
Owner name: REDFLEX TRAFFIC SYSTEMS PTY LTD., ARIZONA
Free format text: RELEASE OF SECURITY INTEREST;ASSIGNOR:NATIONAL AUSTRALIA BANK LIMITED;REEL/FRAME:015056/0798
Effective date: 20040805
|Oct 25, 2006||REMI||Maintenance fee reminder mailed|
|Mar 26, 2007||SULP||Surcharge for late payment|
|Mar 26, 2007||FPAY||Fee payment|
Year of fee payment: 4
|Sep 9, 2008||AS||Assignment|
Owner name: BANK OF MONTREAL, CHICAGO BRANCH, ILLINOIS
Free format text: SECURITY AGREEMENT;ASSIGNOR:REDFLEX TRAFFIC SYSTEMS, INC.;REEL/FRAME:021502/0513
Effective date: 20080602
|Jul 6, 2009||AS||Assignment|
Owner name: REDFLEX TRAFFIC SYSTEMS, INC., ARIZONA
Free format text: RELEASE BY SECURED PARTY;ASSIGNOR:BANK OF MONTREAL, CHICAGO BRANCH;REEL/FRAME:022915/0893
Effective date: 20090630
|Jul 7, 2009||AS||Assignment|
Owner name: CBA CORPORATE SERVICES (NSW) PTY LIMITED ABN 25 07
Free format text: SECURITY AGREEMENT;ASSIGNOR:REDFLEX TRAFFIC SYSTEMS, INC.;REEL/FRAME:022917/0509
Effective date: 20090630
|Nov 15, 2010||REMI||Maintenance fee reminder mailed|
|Mar 30, 2011||FPAY||Fee payment|
Year of fee payment: 8
|Mar 30, 2011||SULP||Surcharge for late payment|
Year of fee payment: 7
|Nov 14, 2014||REMI||Maintenance fee reminder mailed|
|Apr 7, 2015||SULP||Surcharge for late payment|
Year of fee payment: 11
|Apr 7, 2015||FPAY||Fee payment|
Year of fee payment: 12